.png){kind=icon}
返回
A genetic algorithm solution to the unit commitment problem
.png){kind=icon}
PRE
AI
DOI：10.1109/59.485989.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper presents a Genetic Algorithm (GA) solution to the Unit Commitment problem. GAs are general purpose optimization techniques based on principles inspired from; the biological evolution using metaphors of mechanisms such as natural selection, genetic recombination and survival of the fittest. A simple GA algorithm implementation using the standard crossover and mutation operators could locate near optimal solutions but in most cases failed to converge to the optimal solution. However, using the Varying Quality function technique and adding problem specific operators, satisfactory solutions to the Unit Commitment problem were obtained. Test results for systems of up to 100 units and comparisons with results obtained using Lagrangian Relaxation and Dynamic Programming are also reported.
Keyword:
unit commitment
genetic algorithms
期刊
IF:
7.2
论文数:
1.1W
被引数:
5.0W
机构
暂无机构信息
引用论文
Development of Knowledge of Unity of Tuberculosis and of the Portals of Entry of Tubercle Bacilli
PREAI
分享收藏Preoperative open field behavior predicts levels of neuropathic pain-related behavior in mice术前旷场行为预测小鼠神经病理性疼痛相关行为水平
PREAI
分享收藏Differential modulation of synaptic plasticity and local circuit activity in the dentate gyrus and CA1 regions of the rat hippocampus by corticosterone.png)
Stress
OAAI
StressIF0
OAAI分享收藏
查看更多内容